Multiperiod scheduling optimization of postearthquake emergency supply based on real-time environmental information
Bibliographic record
Abstract
The advancement of Internet of Things technology enables the collection and transmission of real-time environmental and vehicle information, aiding the scheduling of postearthquake emergency supplies. Earthquakes often cause victims psychological pain due to insufficient supplies, and secondary disasters during transportation complicate supply scheduling. This study used a questionnaire to determine the psychological pain perception cost function of victims and identify the parameter value ranges under various environmental conditions. A fuzzy inference system was applied to ascertain the function parameters based on actual earthquake losses. Subsequently, a mixed-integer programming model for the multiperiod scheduling of emergency supplies was developed. An improved particle swarm optimization (IPSO) algorithm with a nondominated solution adjustment strategy was devised to solve the model and compared with the traditional particle swarm optimization (PSO) algorithm. The efficacy of the IPSO algorithm was validated through multiple examples. Additionally, a sensitivity analysis of factors such as supply satisfaction proportion was conducted. Results indicated that when remaining supplies fail to meet the minimum needs of undistributed disaster points, setting a minimum satisfaction percentage effectively reduces the total psychological pain cost. This study offers significant theoretical value in alleviating victims' psychological pain and enhancing rescue efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".